By Dr. Raju Gudla, Assistant Professor, Department of Computer Science and Engineering, SRM University -AP (Amaravati)
The transition to sustainable sources of energy, increased efficiency, and better infrastructure depends upon one key element – the creation of new materials. Superconductors, batteries, catalysts, and nuclear energy – whatever kind of materials are used in today’s world, they determine how efficiently the energy is produced, saved, transferred and utilized.Until now, researchers had been using classical computers to model atoms, molecules and materials. However, it is very hard to simulate quantum interactions within a big and complex system. In this regard, quantum computing has become popular. Quantum computers, which work according to the laws of quantum mechanics like superposition and entanglement, can provide unique ways to study the physical systems that are hard to model on classical computers.However, things are moving beyond the theoretical discussion. Now, researchers and technology companies start utilizing quantum computing in superconductivity, fluid dynamics, material deformation, batteries, and catalysis. According to the 2026 perspective in ACS Energy Letters, quantum computing could enhance classical methods of studying physical processes, especially high-dimensional and strongly correlated energy-material problems.
Understanding Superconductivity: The Search for Lossless Electricity
One of the most exciting uses of quantum computers involves research on superconductivity. Superconductors are materials that, under certain conditions, can transmit electrical current with very low or zero resistance. Should it be possible to use superconducting materials at higher and more practical temperatures, it might transform the fields of electricity transmission, electric motor propulsion, magnetism, and energy generation.Quantinuum published an interesting materials science experiment in November 2025 where their Helios trapped-ion quantum computer was used to simulate the non-equilibrium Fermi-Hubbard model in regards to superconductivity. The simulation used up to 90 qubits to simulate a 6×6 lattice, which was a quantum system with an intractably large number of states.It does not matter that quantum computers have made no practical discoveries regarding a room-temperature superconductor yet. What matters is that they provide a completely new way to conduct and simulate quantum experiments.Helios is an example of a 98-qubit quantum computer that has fully connected qubits. The architecture of such a quantum computer was described in a recent Nature article, which highlighted its hardware specifications, such as single- and two-qubit gates and the scaling approach used for this quantum device. At the same time, advances in superconductivity may serve as a foundation for designing materials that can conduct electricity more efficiently, with a much lower loss rate.
Quantum Computing Meets Fluid Dynamics and Nuclear Energy
Quantum computers are explored for problems that rely on partial differential equations (PDEs), which are mathematical models used to model physical processes, such as fluid dynamics, heat transfer, and deformation.The Qiskit quantum computing framework by IBM utilizes QUICK-PDE, offered by ColibriTD, as the quantum function for solving selected PDEs. The framework uses parameterized quantum circuits as trial functions and optimizes them using the hybrid quantum-classical approach. The applications include computational fluid dynamics and material deformation.One of the most interesting examples is the use of quantum computing in the nuclear energy industry. IBM noted that scientists study the QUICK-PDE solution for novel reactive fluids designed to improve heat transfer in Small Modular Reactors (SMRs). A better understanding of heat transfer can play an important role in designing future nuclear energy systems.This is just one of the main characteristics of near-term quantum computing: it does not mean that quantum algorithms have to replace existing engineering software completely. Instead, quantum algorithms can be combined with classical computing that performs standard numerical calculations, while quantum computing solves selected computational parts.
Designing Better Batteries
The second example where material discovery becomes extremely important is related to energy storage. There is a need for better batteries for electric vehicles, renewable energy power grids, portable electronics, and energy storage facilities that have higher energy density, better lifetime, fast charging capacity, and enhanced safety.On the micro scale, performance of batteries is dictated by complicated chemical and physical processes involving ions, electrons, interfaces, and material structures. Quantum chemistry calculations may help to understand these phenomena and to find promising materials before performing expensive laboratory tests.The recent studies try to apply quantum computing in the area that goes beyond theoretical chemistry. In 2026, quantum-kernel learning was applied for battery prognosis using lithium-iron-phosphate cell data. The researchers observed improvements in prediction accuracy and lower amount of required training data in the given experimental setting but significant degradation in computations on noisy quantum computers. Error mitigation methods partially restored the performance.Thus, the problem and the prospect are revealed here. Quantum computing can provide helpful solutions for batteries, but current hardware is noisy and constrained.
Catalysts and Chemical Discovery
Catalysts are a key component in industrial chemistry processes such as hydrogen production, carbon capture, and fuel processing, along with other forms of energy technologies. A catalyst needs to have a good understanding of the electron dynamics in molecules/materials, and chemical reaction kinetics on surfaces.It turns out that quantum computers are extremely promising for such endeavors because chemistry itself is a fundamentally quantum process. In theory, a quantum computer that is powerful enough will be able to perform a simulation of molecular electronic structure and allow researchers to study chemical reactions in unparalleled detail.A 2026 review article published in the Journal of Materials Chemistry A highlights that quantum computers can be a potentially good platform for some electronic-structure problems, specifically those that require simulation of strong electronic correlation, multiple spin states, and nonadiabatic phenomena. The problem, according to the authors, is that many existing quantum computers are only being used as proofs of concept for small or idealized cases.Thus, one should not expect quantum computers to replace computational chemistry as we know it right away. It is more probable that both techniques will complement each other, often working hand in hand.
From Laboratory Experiments to Industrial Impact
The importance of quantum computing for materials science and energy applications is in the potential of reducing the discovery cycle time. The discovery of any material consists of theoretical models, candidate selection, synthesis, laboratory tests, and optimization. By using computational methods to eliminate large numbers of impossible candidates before laboratory synthesis, scientists would be able to save time and resources spent on the development process. Quantum computing might become one part of the mentioned process: Quantum simulation → candidate material → classical modelling → laboratory synthesis → experimental validation → optimized material. AI might contribute to the process by analysing large databases and finding suitable candidates, while quantum computers would have to run high complexity simulations of the selected materials. However, many difficulties remain. First of all, current quantum computers suffer from a low number of qubits, noise, circuit depth, and overhead in computation. Furthermore, proving that a quantum algorithm would solve a particular materials science problem better than a classical approach is still the main task for research.
The Road Ahead
The intersection of quantum computing, artificial intelligence, high-performance computing, and materials science holds the promise of becoming an important technological frontier in the coming years. The range of applications may be diverse, including superconductors for improved electric grid, better materials for energy storage, catalysts for cleaner reactions, fluid and heat transfer simulation for next-generation reactors, and computer-aided discovery of novel materials. Yet the most realistic forecast is one of evolution rather than revolution, since quantum computers will not replace conventional computers in materials science in the near future. Hybrid quantum-classical computation is expected to become gradually preferable for certain tasks where quantum computing gives a computational advantage over conventional computers. In the end, the promise is very attractive: should scientists be able to develop better simulations and designs of matter, quantum computing may not only revolutionize materials discovery but also the production, storage, transmission, and utilization of energy. Thus, the quantum revolution might go beyond the creation of more powerful computers and have an even bigger influence on the discovery of better matter for an energy-efficient world.
